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Jurnal Computer Science and Information Technology (CoSciTech)
Published by Universitas Muhammadiyah Riau
ISSN : 2723567X     EISSN : 27235661     DOI : https://doi.org/10.37859/coscitech
Core Subject : Science,
Computer Science & IT
Jurnal CoSciTech (Computer Science and Information Technology) merupakan jurnal peer-review yang diterbitkan oleh Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Univeritas Muhammadiyah Riau (UMRI) sejak April tahun 2020. Jurnal CoSciTech terdaftar pada PDII LIPI dengan Nomor ISSN 2723-5661 (Online) dan 2723-567X (Cetak). Jurnal CoSciTech berkomitmen menjadi jurnal nasional terbaik untuk publikasi hasil penelitian yang berkualitas dan menjadi rujukan bagi para peneliti. Jurnal CoSciTech menerbitkan paper secara berkala dua kali setahun yaitu pada bulan April dan Oktober. Semua publikasi di jurnal CoSciTech bersifat terbuka yang memungkinkan artikel tersedia secara bebas online tanpa berlangganan.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 374 Documents
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Klasifikasi kendaraan bermotor berdasarkan jumlah gandar menggunakan adaptive minimal ensemble Al Hakim, Abdurrahman; Muttaqin, Faisal; Hendra Maulana
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11239

Abstract

The increasing volume of motor vehicles requires automated monitoring for the classification of heavy vehicle categories (Category I–V) based on the number of axles using side-view cameras. This process represents a complex fine-grained visual classification challenge due to the similar body shapes of trucks. To address the dilemma between the need for high accuracy and computational efficiency, this study implements an Adaptive Minimal Ensemble (AME) architecture that adaptively combines small-scale models.  The model is evaluated using a confusion matrix along with accuracy, precision, recall, and F1-score metrics. The testing results demonstrate that a single EfficientNetV2-S model is only able to achieve a maximum accuracy of 83% and exhibits significant limitations in extracting crucial distinguishing features, leading to misclassification of Category 4 and 5 vehicles. In contrast, the AME architecture, which utilizes the two best-performing EfficientNetV2-S base models, successfully achieves a substantial performance improvement with 95% accuracy, 95.21% precision, 95% recall, and a 94.99% F1-score.  In conclusion, the adaptive layer mechanism in AME is proven to be highly effective in compensating for the individual prediction weaknesses of its base models, resulting in a significantly more precise vehicle classification monitoring system.
Implementasi Algoritma Random Forest pada Web-App Sebagai Instrumen Deteksi Dini Penyakit Diabetes Fauzan, Habibul; Haerani, Elin; Kurnia, Fitra; Yanti, Novi
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11261

Abstract

Diabetes is a chronic metabolic disease and one of the leading causes of death worldwide, with the number of sufferers projected to reach 1.3 billion by 2050. Delayed diagnosis remains a primary challenge, as nearly half of those affected are unaware of their condition in the early stages, thereby increasing the risk of fatal complications. Data mining approaches using classification algorithms have been widely utilized for early screening. However, the development of medical record models is often hindered by imbalanced data, which causes models to be biased toward the majority class and reduces detection sensitivity for the minority class (patients with diabetes). Furthermore, there is a lack of research integrating these predictive models into responsive application interfaces for end-users. Consequently, this study implements Random Forest optimized with the SMOTE (Synthetic Minority Over-sampling Technique) into a web-based application to serve as a practical early detection tool. Random Forest was selected for its ability to handle complex data and reduce the risk of overfitting. The research stages include data preprocessing, balancing training data using SMOTE, model parameter adjustment through hyperparameter tuning with Grid Search, and the development of a client-server architecture using AstroJS and Flask. The evaluation results demonstrate that the use of SMOTE significantly improves the model's ability to identify the minority class. The model achieved a Recall of 75.0% and an overall accuracy of 95.8%, effectively minimizing False Negative errors. The developed application was verified through Black Box Testing and was declared successful as a responsive and accessible early detection tool for both healthcare professionals and the general public.
Implementasi LoRa pada Monitoring Tempat Pembakaran Sampah berbasis Website: Implementation of LoRa in a Website – Based Waste Incinerator site Monitoring System Muhammad Raehan Maulana; Hafiz Muhardi; Midyanti, Dwi Marisa
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11276

Abstract

Waste accumulation is a common problem caused by low public awareness of waste management and suboptimal waste handling by sanitation workers. Waste incineration can serve as a solution, but it has the potential to cause undesirable consequences if not properly supervised; therefore, a monitoring system capable of tracking the waste incineration process in real-time is necessary. With the rapid advancement of technology, wireless technology can be utilized to address this issue. However, most wireless technologies still rely on internet networks, making them less effective in areas with limited internet connectivity. Therefore, this study proposes the use of LoRa technology as a solution for data transmission without reliance on an internet network. The developed system can monitor waste incineration sites using MQ-2 sensors and flame sensors in real-time via a website, utilizing the LoRa SX1278 data transmission system. Implementation results show that the monitoring system can detect residual smoke and fire from waste incineration sites and monitor them up to a distance of 150 meters. Signal quality in LoRa SX1278 transmission is expressed in RSSI (Received Signal Strength Indicator) units, with an “excellent” signal category achieved up to a distance of 40 meters in the tests conducted.
Convolutional Neural Network dengan Arsitektur InceptionV3 untuk Klasifikasi Citra Makanan Berdasarkan Asal Daerah Jawa dan Sumatera Khasanah, Diva Nayla; Firdaus, Rahmad
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11331

Abstract

This study aims to improve the accuracy of classifying traditional food images based on the regions of Java and Sumatra using the Convolutional Neural Network (CNN) algorithm with the InceptionV3 architecture. Traditional foods from these two regions are often difficult to distinguish due to visual similarities. The dataset consists of 472 food images processed through segmentation, augmentation, and rescaling. The InceptionV3 model was selected for its ability to capture complex visual patterns with high efficiency. The training process employed the Adam optimizer, a learning rate of 0.001, and a 50% dropout regularization technique to prevent overfitting. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the model achieved an accuracy of 90.42%.precision of 91.07%, recall of 92.72%, and F1-score of 90%, significantly improving compared to previous research, which only achieved an accuracy of 64% using CNN without a specific architecture. This study is expected to contribute to the preservation of local culinary culture and support the promotion of tourism and technology-based culinary industries in Indonesia.

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